MétaCan
Menu
Back to cohort
Record W3199755649 · doi:10.1080/08911762.2021.1968555

Why Do Russian Consumers Prefer Foreign-Made Products and Brands?

2021· article· en· W3199755649 on OpenAlexaff
José I. Rojas‐Méndez, Julia Kolotylo

Bibliographic record

VenueJournal of Global Marketing · 2021
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsCarleton University
Fundersnot available
KeywordsConsumer ethnocentrismConceptualizationNomological networkScale (ratio)Construct (python library)MarketingVariance (accounting)EthnocentrismDominance (genetics)AdvertisingExternal validityBusinessPsychologySocial psychologyComputer science

Abstract

fetched live from OpenAlex

This study’s purpose is four-fold: (1) To test the nomological validity of the X-Scale; (2) To assess the bi-dimensionality of the consumer xenocentrism construct in a new geographical setting; (3) To determine the predictive validity of consumer xenocentrism on the willingness to buy foreign-made products and preferences for foreign brands; and (4) To analyze the relationship between consumer xenocentrism and demographics variables. The conceptualization of this research is based on System Justification Theory and Social Dominance Theory. The study was carried out across Russia employing an online survey. Results support the bi-dimensionality of the X-Scale, and indicate that consumer xenocentrism is negatively related to the consumer disposition of ethnocentrism and positively with cosmopolitanism. The X-scale explains significantly more variance on foreign brand preferences than its related consumer dispositions. In addition, consumer xenocentrism is manifested toward preferences for products and brands coming from developed countries instead of developing or transitional ones. Russian consumers are xenocentric, and the ones significantly scoring higher in this construct are males and those speaking more than one language. These results lead to a discussion of the nature of Russian consumer xenocentrism and its potential managerial implications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.323
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Global MarketingSame topicCultural Differences and ValuesFrench-language works237,207